Case study 01 / Pricing
Competitive pricing & promotional impact analysis
Replacing manual price-chasing with historical benchmarks, guardrails and an automated competitor-mining engine for an anonymised national office-supplies retailer.
Anonymised for confidentiality — no client data, prices, logos or systems are shown.
01 / The question
How do you price a full catalogue without chasing the market every day?
Pricing was manual and reactive. There was no consistent, historical view of how price changes had affected volume and revenue, no guardrails around how far a price should move, and no fast way to see where the market sat for key products. Every pricing cycle meant manually checking the market instead of working from a benchmarked baseline.
The question was less "what is the perfect price?" and more operational: how do we set defensible prices quickly, and surface only the exceptions that actually need a human decision?
02 / The approach
Four pieces: benchmarks, guardrails, exceptions, market mining.
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Historical benchmarking
Analysed historical price changes against outcomes to estimate elasticity and sensitivity to price movement. This turned past pricing behaviour into a reference range rather than a single opinion.
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Pricing guardrails
Set ceilings and floors for price changes, grounded in those benchmarks. Routine moves could be made within the band; anything outside it was treated as a deliberate exception.
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Exception workflow
Items that breached the guardrails were flagged for manual approval. This kept out-of-band changes visible and reviewable instead of buried in a spreadsheet.
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Competitor-mining engine
For key products that require market alignment, built a data-mining engine that runs price comparisons — so those products could be checked consistently instead of manually.
03 / Promotional windows
Promotions were measured, not assumed.
Promotional windows were analysed as a separate pricing event: what moved during the window, what happened after it, and whether the discount depth was justified by the response. That separated a genuine demand lift from a promotion that simply traded margin for volume that would have happened anyway.
04 / What changed
Pricing moved from routine market-chasing to a benchmarked process.
- Routine price changes could be set from a historical baseline instead of manually checking the market each cycle.
- Out-of-band changes surfaced automatically for approval, so exceptions stayed visible.
- Key products stayed aligned to the market through a repeatable mining process.
- Promotional windows were evaluated on impact rather than applied by habit.
The practical outcome was a material reduction in the manual effort required to set prices, without removing human control over the decisions that mattered.
05 / Limitations
What this analysis is — and isn't.
Elasticity and sensitivity estimates come from historical, observational data, not controlled experiments, so promotional effects are partly confounded by seasonality and competitor moves. Guardrails are only as good as their calibration and need periodic review. No client figures, product names or system screenshots are published here.